Implementation of Missing Data Imputation Schemes in Face Recognition Algorithm under Partial Occlusion

Abstract

Face detection and recognition algorithms usually assume an image captured from a controlled environment. However, this is not always the case, especially in crowd control under surveillance or footage from a crime scene, where partial occlusions are unavoidable. Unfortunately, these occlusions have an adverse e ect on the performance of these classical recognition algorithms. In this study, the performance of some selected data imputation schemes is evaluated on SVD/PCA frontal face recognition algorithm. e experiment was done on two datasets: Ja e and MIT-CBCL, with immediate con rmation of the adverse e ect of occlusion on the facial algorithm without implementing the imputation scheme. Further experimentation shows that IA is an ideal missing data imputation scheme that works best with the SVD/PCA facial recognition algorithm.